Show simple item record

dc.contributor.advisorMarco F. Ramoni and Isaac S. Kohane.en_US
dc.contributor.authorAlterovitz, Gil, 1975-en_US
dc.contributor.otherHarvard University--MIT Division of Health Sciences and Technology.en_US
dc.date.accessioned2006-11-07T12:27:17Z
dc.date.available2006-11-07T12:27:17Z
dc.date.copyright2005en_US
dc.date.issued2006en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/34479
dc.descriptionThesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, February 2006.en_US
dc.descriptionIncludes bibliographical references (leaves 73-85).en_US
dc.description.abstractProteomics has been revolutionized in the last couple of years through integration of new mass spectrometry technologies such as -Enhanced Laser Desorption/Ionization (SELDI) mass spectrometry. As data is generated in an increasingly rapid and automated manner, novel and application-specific computational methods will be needed to deal with all of this information. This work seeks to develop a Bayesian framework in mass-based proteomics for protein identification. Using the Bayesian framework in a statistical signal processing manner, mass spectrometry data is filtered and analyzed in order to estimate protein identity. This is done by a multi-stage process which compares probabilistic networks generated from mass spectrometry-based data with a mass-based network of protein interactions. In addition, such models can provide insight on features of existing models by identifying relevant proteins. This work finds that the search space of potential proteins can be reduced such that simple antibody-based tests can be used to validate protein identity. This is done with real proteins as a proof of concept. Regarding protein interaction networks, the largest human protein interaction meta-database was created as part of this project, containing over 162,000 interactions. A further contribution is the implementation of the massome network database of mass-based interactions- which is used in the protein identification process.en_US
dc.description.abstract(cont.) This network is explored in terms potential usefulness for protein identification. The framework provides an approach to a number of core issues in proteomics. Besides providing these tools, it yields a novel way to approach statistical signal processing problems in this domain in a way that can be adapted as proteomics-based technologies mature.en_US
dc.description.statementofresponsibilityby Gil Alterovitz.en_US
dc.format.extent89 leavesen_US
dc.format.extent13047525 bytes
dc.format.extent13052064 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/pdf
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582
dc.subjectHarvard University--MIT Division of Health Sciences and Technology.en_US
dc.titleA Bayesian framework for statistical signal processing and knowledge discovery in proteomic engineeringen_US
dc.typeThesisen_US
dc.description.degreePh.D.en_US
dc.contributor.departmentHarvard University--MIT Division of Health Sciences and Technology
dc.identifier.oclc70784395en_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record